Why Adding Followers Alone Won’t Build Your Community

Lately there seems to be an ever increasing amount of silly offers to increase your social media following. Not only is this sleazy, it betrays a complete lack of understanding of how social networks function.

The evidence is clear: the quality of the communities you build is much more important than the size of your following.

Yet things didn’t really get rolling until 1998, when a young graduate student at Cornell named Duncan Watts came up with the idea of small world networks, which formed the basis of our current understanding of how social networks function.

If we want to understand how to build worthwhile communities, Watts’ research is the best place to start.

How Far Information Can Travel

When we consider communicating through social networks, we usually think about it working like this Farrah Fawcett commercial from the 80’s, where she tells two friends about a shampoo and they tell two friends and so on, and so on…

Network theorists have a metric for this effect called the average path length, which calculates the average number of degrees of separation between any two nodes in a network. The smaller the average path length, the less “and so ons” you will need to reach a lot of people.

It should be clear that adding random followers will most likely increase path lengths, not decrease them, so their presence alone will not increase quality of your network.

Clustering, Broken Bottles and Bar Fights

When I was young I lived on a street where I knew all the other kids and their parents. (I was more popular with the kids than the parents). Whenever I got into trouble, all the parents would find out about it and tell their kids not to play with me. That happened a lot.

Things became changed when I got a job and moved to New York City. There, I lived on a block with far more people, but very few knew me or each other. I could go out all night, get into bar fights, carouse with disreputable women and no one would blink an eye.

In the vast network of Manhattan, information wouldn’t travel nearly as far or as fast as back home, even though I came into contact with many more people.

The reach of your network is only one aspect, how it is clustered is far more important. Watts found a simple way to measure this called the clustering coefficient, which is simply the number of links in your network as a percentage of the total potential links you can have.

Closing the Triad

It was this type of thing that Duncan Watts started to think about. In researching his PhD. dissertation at Cornell, he came across Anatol Rapoport’s work and the problem of triadic closure, which can be stated as: If a person knows two other people, what is the chance that those people will get to know each other?

He then came up with a mathematical variable which he called “alpha” that approximated the process of networks developing through triadic closure.

He started with the extreme cases: One, like my childhood neighborhood, where if you know two people, they automatically know each other. The second, more like Manhattan, where you can know two people and will very likely never meet. Then he approximated paths somewhere in between.

His next step was to see what happened to the network as people began connecting with each other. What he found surprised him.

As communities begin to connect, social distance increases (albeit across a larger total community), but then suddenly crash down. Although counter-intuitive, this makes sense in light of the triadic closure concept. People start connecting to each other’s networks and form new clusters organically.

Adding a Little Spice to the Soup

The problem with the alpha model is that it’s really hard to say what “alpha” actually is and relate it to the real world. So Watts came up with a new variable, randomness of connections, and analyzed how that would affect networks.

It works like this: Imagine that you are in a football stadium standing in a ring of 1000 people. You could pass information to those close to you, but it would take a long time for a message to get across the stadium (e.g. figure on the far left).

However, if you randomly passed out a few walkie-talkies, connectivity would increase dramatically. If you went still further and added hundreds of connections, you would make one enormous cluster, but whatever information you wanted to pass on would probably get lost in the din.

Just like with the alpha model, Watts then tested the beta model to see how random connections would affect the network as it developed.

What he found this time was even more surprising than before. Not only does social distance fall very fast, but clustering stays high for quite some time. So, you really can have the best of both worlds: A network that retains the intimacy of small clusters yet still has massive reach.

What’s even more interesting is that it takes just a few random connections to bring social distance down dramatically (about 1/10th of 1% of total links). Far from being difficult to achieve, effective communities develop as a natural process. However, too many random connections (over 10%) can actually decrease connectivity.

Okay, it took us a while to get here, but now we can see the sham. Unless you already have over 100,000 links, adding 10,000 random connections might actually do more harm than good.

Much like I lost connectivity when I moved to Manhattan, your network will probably transmit information worse, not better, if you artificially add too many followers in a short period time.

Common Sense Communities

Far from being responsive to quickie schemes, building communities requires a good sense approach in order to be successful. Here are a few simple rules of thumb:

Context: People will only join and be active in communities that interest them. There’s no sense in adding people to your community who don’t want to be there.

Openness: While it makes no sense to trick people into joining your community, you should make it easy to join. Twitter is well set up for this (anybody can follow anybody), paywalls are not.

Capability: Perhaps the most important thing to realize about communities is that it needs to be easy for people to participate. We often think of online social networking as limited to social media sites. However, simple online features such as comments and ratings can sometimes be even more effective.

With all the excitement about the emergence of online social networks, it’s easy to get carried away. There are so many new ideas and new capabilities, it can be tough to separate the wheat from the chaff, but one thing is clear – buying followers is as stupid as it is sleazy.

There’s no “optimal” size for a network. They can function very well even if they are huge, as long as they are clustered enough to allow for intimate interaction.

The operative metric is the “clustering coefficient” which is fairly easy to calculate. It’s just the number of links as a percentage of total links (n(n-1)/2).

To get a sense of it, think about how the Grateful Dead network functioned differently than most rock band networks. Most rock concerts you go to are very unclustered – you go there for one night with a bunch of people you don’t know and won’t see again.

The Dead, however, toured so consistently that people would follow them and formed groups among themselves who knew each other and there were lots of interconnections between those groups. That added a special feel to the dead shows. It was more than just the music, there was a palpable community there.

Even though the network was enormous, if you were a deadhead, you could go to a show almost anywhere and run into people you knew and who knew others. They would introduce you and the high level of clustering would persist.

So it’s not the size of the network, but how it develops. The more internal links you develop, the more random connections you can take on and still preserve the community.

Thanks for the recommendation. The charts were actually taken from Watt’s Small Worlds, which I don’t usually recommend because of it’s difficulty. However, he wrote a popular version called 6 Degrees which is easy to get through and a must read!

I am always delighted and amazed by the richness of your content. I hope you have a huge following because your style of authentic, grounded material is a type of mentoring this planet could use more of in our time. You are so easy to read and I love the meshing of the straight shooting simple wisdom with the multi-layered research material.
Just another comment of gratitude for me.

I think my interest in DigitalTonto is inspired by the value of its contents that enrich my professional knowledge. The desire to share knowledge with each other in a group of people is far more important than knowing each other. Average Path Length will, therefore, be determined, please correct me if I am wrong, by the frequency of and consistence in interaction due to the value of knowledge the people in a group exchange with each other or sharing of the experiences emanating from similar interests. This benefit of the value of knowledge exchanged and sharing of the experiences keep the people together adding “commercial value” to their numerical and professional strength for the advertisers and commercial organizations. This commercial value enhances the financial viability of the platform that provides an opportunity to the group for interacting with each other without any interruption. This uninterrupted interaction provides a solid foundation to a community for carrying out their activities.

Emergence and sizes of social networks are not important. The profile and quality of the views of those who interact at these platforms is monitored and evaluated by the advertisers and they automatically start patronizing such networks. However, some platforms do not allow access to advertiers for keeping the privacy of their members intact. I think DigitalTonto is one of such platforms!!!

I think that’s basically right, networks that continue to grow links internally will do significantly better than networks that don’t.

However, I would be careful how we use the term “quality.” There are a lot of reasons for information to spread and it often doesn’t have anything to do with what we would ordinarily associate with merit. I’m going to address this issue in an upcoming post about memes.

Thanks for all of your comments. It really helps to have active readers!

I wondered whether you’ve come across any research material or other data that shares insights on networks in the physical/real/natural world and their associated clustering coefficient? e.g. do universities in general have a higher clustering coefficient than high schools? what about at companies etc or even digital networks? How can one find network clustering data n the wild?

It’d be great to relate this to existing networks or access the data to learn further about the theory in real world.